{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/timers-document-level-temporal-relation","title":"TIMERS: Document-level Temporal Relation Extraction","arxiv_id":null,"date":"2021-08-01","proceeding":"ACL 2021 5","authors":["Puneet Mathur","Rajiv Jain","Franck Dernoncourt","Vlad Morariu","Quan Hung Tran","Dinesh Manocha"],"abstract":"We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language. Our proposed method leverages rhetorical discourse features and temporal arguments from semantic role labels, in addition to traditional local syntactic features, trained through a Gated Relational-GCN. Extensive experiments show that the proposed model outperforms previous methods by 5-18{\\%} on the TDDiscourse, TimeBank-Dense, and MATRES datasets due to our discourse-level modeling.","url_abs":"https://aclanthology.org/2021.acl-short.67","url_pdf":"https://aclanthology.org/2021.acl-short.67.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-classification","task_name":"Relation Classification"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"temporal-relation-classification","task_name":"Temporal Relation Classification"},{"task_slug":"temporal-relation-extraction","task_name":"Temporal Relation Extraction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/temporal-relation-classification-on-matres","task":"Temporal Relation Classification","dataset":"MATRES","model":"TIMERS","rank_in_archive_order":4,"of":4,"metrics":{"F1":"82.3"},"uses_additional_data":false},{"leaderboard":"/sota/temporal-relation-classification-on-tb-dense","task":"Temporal Relation Classification","dataset":"TB-Dense","model":"TIMERS","rank_in_archive_order":3,"of":3,"metrics":{"F1":"67.8"},"uses_additional_data":false},{"leaderboard":"/sota/temporal-relation-classification-on-tddauto","task":"Temporal Relation Classification","dataset":"TDDAuto","model":"TIMERS","rank_in_archive_order":3,"of":3,"metrics":{"F1":"71.1"},"uses_additional_data":false},{"leaderboard":"/sota/temporal-relation-classification-on-tddman","task":"Temporal Relation Classification","dataset":"TDDMan","model":"TIMERS","rank_in_archive_order":3,"of":3,"metrics":{"F1":"45.5"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}